Computing hardware is able to perform greater numbers of computational tasks at faster rates of speed. But the improvements in computer machines and components, such as the implementation of multiple processing units (e.g., central processing units (“CPUs”), graphics processing units, (“GPUs”), etc.), advanced memory devices, and peripheral devices, increasingly are hindered by a human user in the computational loop. In particular, conventional interfaces between human users and computational machines, while functional, are suboptimal for human users to effectively guide the rapid execution of instructions and to consume the information derived therefrom. Therefore, the bandwidth of human user interactions with conventional interfaces, such as keyboards, mice, and other known data-entry devices, are “bottlenecks” that impede the capabilities of known computational machines.
A variety of approaches to provide interfaces to computing machines, while functional, suffer a number of drawbacks. For example, speech recognition applications have been developed with an aim to improve the rate of inputting data over the use of standard keyboards. But typical speech recognition applications are limited by the inaccuracies of interpreting the spoken word and the rate at which human users are able to speak and correct errors, as well as other inefficiencies.
In one approach, conventional diagnostic machines and techniques have been used to derive information from a brain of human to infer a thought so as to, for example, provide an input or request. In some traditional implementations, known sensing techniques are used to coarsely sense a brain activity with limited accuracy. Examples of known sensing techniques relate to magnetoencephalography (“MEG”), magnetic resonance imaging (“MRI”), electroencephalography (“EEG”), electrical impedance tomography (“EIT”), etc. Generally, these techniques are designed to generate imagery (e.g., 3-D images) of a brain or other portions of a human body.
Magnetoencephalography techniques rely on detecting naturally-occurring, intrinsic magnetic fields produced by a brain and its neural currents. Magnetoencephalography, however, requires the use of immobile superconducting quantum interference devices (“SQUID”), which is a drawback in connection with conventional equipment, to detect the relatively very small magnitudes of magnetic fields. The SQUID sensors require cryogenics (e.g., liquid helium at −270° C. or colder), which limits the usage to certain cases due to, for example, the size of magnetoencephalography equipment, and has relatively coarse resolution. A further drawback is that magnetoencephalography equipment, including SQUID sensors, requires relatively large amounts of capital expenditures. A predominant drawback of measuring the naturally-occurring, intrinsic magnetic fields produced by a brain requires the complexities of measuring very, very small magnetic fields, which presents challenges of working with such magnetic fields. In some cases, naturally-occurring, intrinsic magnetic fields produced by a brain may be millions times weaker than the earth's magnetic field.
Magnetic resonance imaging techniques typically employ relatively large magnetic fields and are used principally to generate medically diagnostic imagery of the brain, and, thus suffer some drawbacks when used to detect physiological activity. For example, magnetic resonance imaging machinery generally is limited to tracking indirect effects (e.g., non-neural activity) of a brain, such as blood flow and glucose uptake. As such, the temporal resolution of magnetic resonance imaging techniques is typically low. Another drawback is that magnetic resonance imaging requires relatively large amounts of capital expenditures and immobile equipment that limits usage to predetermined locations, such as medical offices. Electroencephalography techniques typically monitor the naturally-occurring, intrinsic electrical activity as “brain wave.” Common approaches typically use small number of electrodes that are sampled relatively slowly. Further, the signal-to-noise ratios of the sensed signals are generally insufficient. For example, an electroencephalography technique may use 256 electrodes that requires contact with the scalp, whereby the signals are sampled at 100 Hz. Thus, the spatial and/or temporal resolution may be less optimal in various implementations. Electrical impedance tomography is a medical imaging technique that employs electrodes with which to determine an impedance of various biological tissues. Principally, electrical impedance tomography requires injecting a current into tissue and sensing a current or voltage from the tissue, whereby the received current or voltage includes impedance information. There are a number of drawbacks with this approach. For example, electrical impedance tomography techniques rely on using electrodes that require contact with the scalp (e.g., the electrodes receiving the electric current impedance data typically are required to be in contact with skin), and the relatively low magnitudes of current that provides for suboptimal temporal resolution, among other things. Further, spatial resolution associated with this approach is limited by the number of electrodes commonly used.
Furthermore, conventional approaches provide data at relatively coarse granularity using relatively high levels of features and low levels of resolution and, thus, are not well-suited to spatially and temporally characterize and correlate neuronal activity, as well as other physiological activity, to identify an intent, thought, or command associated with an organism. Existing man-machine interfaces based on conventional approaches generally are slower, more complex, and typically are associated with latency amounts which render such interfaces impractical for many tasks or uses. For example, some conventional sensing techniques require a determination of a 3-D model of a brain with which to spatialize. These sensing techniques typically have relatively low bandwidths, such as those of EEGs, which relay on high-level features such as alpha and gamma waves and extract coarse levels of data that have limited usage. Existing techniques often require a user to learn to produce a number of easily detectable brain activity patterns, as for example in “biofeedback” techniques, and this limits the range of possible applications.
Thus, what is needed is a solution for facilitating an interface for human users and computational machines, without the limitations of conventional techniques.